Repairing Numerical Equations in Analogically Blended Theories Using Reformation

Cheng-Hao Cai, Alan Bundy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The ABC system supports analogical abduction algorithms for knowledge transfer, e.g., existing logical rules are adapted, by reformation, into new rules by substituting symbols for similar ones. Although such knowledge transfer method can easily expand knowledge sets, it is likely to produce inconsistent knowledge, e.g., equations that do not fit target data in the real world. To solve this problem, we extend the classic reformation algorithm to repair numerical equations that violate target data. Equation reformation is achieved by weakening equation parameters when unblocking failed SLD-resolution proofs. The feasibility of numerical equation reformation is demonstrated by the automated repair of a faulty electrostatic force equation that is analogically transferred from the gravity equation
Original languageEnglish
Title of host publicationProceedings of the Human-Like Computing Workshop 2022
EditorsAlan Bundy, Denis Mareschal
PublisherCEUR Workshop Proceedings (
Number of pages6
Publication statusPublished - 2 Oct 2022
EventThe 3rd International Workshop on Human-Like Computing 2022 - Windsor, United Kingdom
Duration: 28 Sept 202230 Sept 2022
Conference number: 3

Publication series

NameHuman-Like Computing Workshop 2022
PublisherCEUR Workshop Proceedings
ISSN (Electronic)1613-0073


WorkshopThe 3rd International Workshop on Human-Like Computing 2022
Abbreviated titleHLC 2022
Country/TerritoryUnited Kingdom
Internet address

Keywords / Materials (for Non-textual outputs)

  • Reformation
  • Theory repair
  • Analogical reasoning
  • Knowledge transfer


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